probability_discount {bayesDP}

Bayesian Discount Prior: Comparison Between Current and Historical Data

Description

probability_discount can be used to estimate the posterior probability of the comparison between historical and current data in the context of a clinical trial with normal (mean) data. probability_discount is not used internally but is given for educational purposes.

Usage

probability_discount(
  mu = NULL,
  sigma = NULL,
  N = NULL,
  mu0 = NULL,
  sigma0 = NULL,
  N0 = NULL,
  number_mcmc = 10000,
  method = "fixed"
)

Arguments

mu

scalar. Mean of the current data.

sigma

scalar. Standard deviation of the current data.

N

scalar. Number of observations of the current data.

mu0

scalar. Mean of the historical data.

sigma0

scalar. Standard deviation of the historical data.

N0

scalar. Number of observations of the historical data.

number_mcmc

scalar. Number of Monte Carlo simulations. Default is 10000.

method

character. Analysis method. Default value "fixed" estimates the posterior probability and holds it fixed. Alternative method "mc" estimates the posterior probability for each Monte Carlo iteration. See the the bdpnormal vignette
vignette("bdpnormal-vignette", package="bayesDP") for more details.

Details

This function is not used internally but is given for educational purposes. Given the inputs, the output is the posterior probability of the comparison between current and historical data in the context of a clinical trial with normal (mean) data.

Value

probability_discount returns an object of class "probability_discount".

An object of class probability_discount contains the following:

p_hat

scalar. The posterior probability of the comparison historical data weight. If method="mc", a vector of posterior probabilities of length number_mcmc is returned.

References

Haddad, T., Himes, A., Thompson, L., Irony, T., Nair, R. MDIC Computer Modeling and Simulation working group.(2017) Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. Journal of Biopharmaceutical Statistics, 1-15.

Examples

probability_discount(
  mu = 0, sigma = 1, N = 100,
  mu0 = 0.1, sigma0 = 1, N0 = 100
)